People often consume media content in groups, in a shared setting. For example, the members of a family may watch TV, DVDs or on-demand video services together; and a group of friends may listen to music together. This is particularly common in households in some emerging economies, where an entire family may share access to a single device, such as a TV or personal computer. In such a shared setting, it is desirable for the group to be able to find content that has a greater chance of being liked by everyone.
In the past, websites have used tools such as collaborative filtering to generate recommendations for individual users, based on their existing consumption patterns or feedback (such as ratings) received in response to content previously consumed. However, the group recommendation scenario presents new and different challenges to those of generating recommendations for individual users.